Bias and Variance in Machine Learning: Striking the Right Balance In the vast landscape of machine learning, two critical concepts play a pivotal role in shaping the performance of our models: bias and variance. These two adversaries often engage in a delicate dance, influencing how well our models generalize to unseen data. Let’s explore their nuances, understand their impact, and discover strategies to strike the right balance. 1. Bias: The Underfitting Culprit Bias represents the model’s inability to capture the underlying complexity of the data. Imagine assuming that the data follows a simple linear function when, in reality, it dances to a more intricate tune. 💡 Here’s what you need to know about bias: Bias occurs due to incorrect assumptions during model training. √Effect: High bias leads to underfitting, where the model oversimplifies the problem and fails to capture essential patterns. √Complexity Boost: Consider using a more complex model (e.g., deep neural networks with additional hidden layers) to better fit the data. √Feature Expansion: Add more features to enhance the model’s ability to capture underlying trends. √Regularization: Adjust regularization strength (e.g., L1 or L2 regularization) to prevent overfitting. 2. Variance: The Overfitting Nemesis Variance, on the other hand, emerges from the model’s sensitivity to variations in the training data. It craves complexity, but too much of it can lead to overfitting. 📑 Here’s the scoop on variance: Variance arises when the model is too sensitive to training data fluctuations. 💡Effect: High variance results in overfitting, where the model fits the training data perfectly but struggles with unseen data. 📊Simplicity Check: Opt for simpler models to reduce variance. 📈Regularization: Strengthen regularization to tame the model’s wild fluctuations. 📑More Data: Gather more training data to stabilize the model’s behavior. 📃The Bias-Variance Tradeoff Ah, the delicate balance! The bias-variance tradeoff dictates that as we reduce bias, variance tends to rise, and vice versa. Our goal? Find the sweet spot where both errors are minimized. 🎯 Conclusion: In the grand symphony of machine learning, bias and variance dance together, shaping our models’ destiny. Remember, a dash of bias and a sprinkle of variance can lead to a harmonious melody of predictive power. 🎶 #machinelearning #biasandvariance #datascience #modeling #underfitting #overfitting #lassoridgeregression
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Forensic technology services| Disputes and Investigation consulting| Digital Forensic | Computer Science Engineer
This makes it a lot easier to firstly understand what are the possible applications of machine learning and what are the available algorithms out there !! What ML algo do you use and for what purpose?
PhD candidate specializing in AI research | Academic Reviewer | Academic Writer | Machine Learning | Deep Learning | NLP | LLMs | RAG | Computer Vision | Medical Data Analysis
𝐓𝐨𝐩 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐚𝐧𝐝 𝐓𝐡𝐞𝐢𝐫 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Machine Learning (ML) is revolutionizing various industries, and understanding its core concepts is crucial. Here, I explore ML algorithms and their applications for three major tasks: Classification, Prediction, and Clustering, as follows: 1. 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 - Here, we assess how well an AI model categorizes data. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 • Decision Trees: Rule-based models for making classifications (e.g., spam or not spam). • Random Forest: Combines multiple decision trees for improved accuracy. • Support Vector Machines (SVM): Efficiently classifies data into categories. • K-Nearest Neighbors (KNN): Classifies data based on the majority vote of its neighbors. • Naive Bayes: Classifies data based on probabilities of features being present in a class. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 From fraud detection and medical diagnosis to image recognition and recommendation systems, classification powers diverse tasks. ------------------------------------------------------------ 2. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 - This focuses on how well an AI model forecasts future values. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 • Linear Regression: Models linear relationships between variables for continuous prediction (e.g., future sales based on historical trends). • Logistic Regression: Predicts the probability of an event occurring (e.g., customer churn). • Gradient Boosting Machines (GBM): Powerful ensemble models for complex predictions. • Neural Networks: Inspired by the human brain, they learn complex patterns from data for prediction. • ARIMA (AutoRegressive Integrated Moving Average): Models time series data for forecasting. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Prediction impacts numerous areas, including sales forecasting, stock market analysis, and medical prognosis. ------------------------------------------------------------ 3. 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 - is the process of grouping similar data points together in order to discover inherent patterns or structures within a dataset. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 Placeholder for missing information: Explore algorithms like K-Means (grouping data points based on similarity), Hierarchical Clustering (building a hierarchy of clusters), DBSCAN (identifying clusters of varying densities), Gaussian Mixture Models (representing data as a mixture of probability distributions), and Agglomerative Clustering (a bottom-up approach to forming clusters). 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Clustering empowers tasks like customer segmentation, image segmentation, anomaly detection, and social network analysis. ------------------------------------------------------------ Good luck with your AI research! #phd #phdlife #phdthesis #research #researchpaper #ai #machinelearning #deeplearning #academicwriting #ai #machinelearning #deeplearning #genai #ann #nlp #DataScience #artificialintelligence #python #pythonprogramming
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Data Scientist: Python | Machine Learning | Cloud Computing | NLP #MachineLearning #AmazonSageMaker #AmazonRekognition #AmazonComprehend
A simple presentation yet Informative. You'll appreciate the effort of good work in the simplicity of the presentation.
PhD candidate specializing in AI research | Academic Reviewer | Academic Writer | Machine Learning | Deep Learning | NLP | LLMs | RAG | Computer Vision | Medical Data Analysis
𝐓𝐨𝐩 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐚𝐧𝐝 𝐓𝐡𝐞𝐢𝐫 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Machine Learning (ML) is revolutionizing various industries, and understanding its core concepts is crucial. Here, I explore ML algorithms and their applications for three major tasks: Classification, Prediction, and Clustering, as follows: 1. 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 - Here, we assess how well an AI model categorizes data. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 • Decision Trees: Rule-based models for making classifications (e.g., spam or not spam). • Random Forest: Combines multiple decision trees for improved accuracy. • Support Vector Machines (SVM): Efficiently classifies data into categories. • K-Nearest Neighbors (KNN): Classifies data based on the majority vote of its neighbors. • Naive Bayes: Classifies data based on probabilities of features being present in a class. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 From fraud detection and medical diagnosis to image recognition and recommendation systems, classification powers diverse tasks. ------------------------------------------------------------ 2. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 - This focuses on how well an AI model forecasts future values. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 • Linear Regression: Models linear relationships between variables for continuous prediction (e.g., future sales based on historical trends). • Logistic Regression: Predicts the probability of an event occurring (e.g., customer churn). • Gradient Boosting Machines (GBM): Powerful ensemble models for complex predictions. • Neural Networks: Inspired by the human brain, they learn complex patterns from data for prediction. • ARIMA (AutoRegressive Integrated Moving Average): Models time series data for forecasting. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Prediction impacts numerous areas, including sales forecasting, stock market analysis, and medical prognosis. ------------------------------------------------------------ 3. 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 - is the process of grouping similar data points together in order to discover inherent patterns or structures within a dataset. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 Placeholder for missing information: Explore algorithms like K-Means (grouping data points based on similarity), Hierarchical Clustering (building a hierarchy of clusters), DBSCAN (identifying clusters of varying densities), Gaussian Mixture Models (representing data as a mixture of probability distributions), and Agglomerative Clustering (a bottom-up approach to forming clusters). 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Clustering empowers tasks like customer segmentation, image segmentation, anomaly detection, and social network analysis. ------------------------------------------------------------ Good luck with your AI research! #phd #phdlife #phdthesis #research #researchpaper #ai #machinelearning #deeplearning #academicwriting #ai #machinelearning #deeplearning #genai #ann #nlp #DataScience #artificialintelligence #python #pythonprogramming
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PhD candidate specializing in AI research | Academic Reviewer | Academic Writer | Machine Learning | Deep Learning | NLP | LLMs | RAG | Computer Vision | Medical Data Analysis
𝐓𝐨𝐩 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐚𝐧𝐝 𝐓𝐡𝐞𝐢𝐫 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Machine Learning (ML) is revolutionizing various industries, and understanding its core concepts is crucial. Here, I explore ML algorithms and their applications for three major tasks: Classification, Prediction, and Clustering, as follows: 1. 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 - Here, we assess how well an AI model categorizes data. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 • Decision Trees: Rule-based models for making classifications (e.g., spam or not spam). • Random Forest: Combines multiple decision trees for improved accuracy. • Support Vector Machines (SVM): Efficiently classifies data into categories. • K-Nearest Neighbors (KNN): Classifies data based on the majority vote of its neighbors. • Naive Bayes: Classifies data based on probabilities of features being present in a class. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 From fraud detection and medical diagnosis to image recognition and recommendation systems, classification powers diverse tasks. ------------------------------------------------------------ 2. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 - This focuses on how well an AI model forecasts future values. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 • Linear Regression: Models linear relationships between variables for continuous prediction (e.g., future sales based on historical trends). • Logistic Regression: Predicts the probability of an event occurring (e.g., customer churn). • Gradient Boosting Machines (GBM): Powerful ensemble models for complex predictions. • Neural Networks: Inspired by the human brain, they learn complex patterns from data for prediction. • ARIMA (AutoRegressive Integrated Moving Average): Models time series data for forecasting. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Prediction impacts numerous areas, including sales forecasting, stock market analysis, and medical prognosis. ------------------------------------------------------------ 3. 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 - is the process of grouping similar data points together in order to discover inherent patterns or structures within a dataset. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 Placeholder for missing information: Explore algorithms like K-Means (grouping data points based on similarity), Hierarchical Clustering (building a hierarchy of clusters), DBSCAN (identifying clusters of varying densities), Gaussian Mixture Models (representing data as a mixture of probability distributions), and Agglomerative Clustering (a bottom-up approach to forming clusters). 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Clustering empowers tasks like customer segmentation, image segmentation, anomaly detection, and social network analysis. ------------------------------------------------------------ Good luck with your AI research! #phd #phdlife #phdthesis #research #researchpaper #ai #machinelearning #deeplearning #academicwriting #ai #machinelearning #deeplearning #genai #ann #nlp #DataScience #artificialintelligence #python #pythonprogramming
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𝐓𝐨𝐩 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐚𝐧𝐝 𝐓𝐡𝐞𝐢𝐫 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Machine Learning (ML) is revolutionizing various industries, and understanding its core concepts is crucial. Here, I explore ML algorithms and their applications for three major tasks: Classification, Prediction, and Clustering, as follows: 1. 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 - Here, we assess how well an AI model categorizes data. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 • Decision Trees: Rule-based models for making classifications (e.g., spam or not spam). • Random Forest: Combines multiple decision trees for improved accuracy. • Support Vector Machines (SVM): Efficiently classifies data into categories. • K-Nearest Neighbors (KNN): Classifies data based on the majority vote of its neighbors. • Naive Bayes: Classifies data based on probabilities of features being present in a class. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 From fraud detection and medical diagnosis to image recognition and recommendation systems, classification powers diverse tasks. ------------------------------------------------------------ 2. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 - This focuses on how well an AI model forecasts future values. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 • Linear Regression: Models linear relationships between variables for continuous prediction (e.g., future sales based on historical trends). • Logistic Regression: Predicts the probability of an event occurring (e.g., customer churn). • Gradient Boosting Machines (GBM): Powerful ensemble models for complex predictions. • Neural Networks: Inspired by the human brain, they learn complex patterns from data for prediction. • ARIMA (AutoRegressive Integrated Moving Average): Models time series data for forecasting. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Prediction impacts numerous areas, including sales forecasting, stock market analysis, and medical prognosis. ------------------------------------------------------------ 3. 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 - is the process of grouping similar data points together in order to discover inherent patterns or structures within a dataset. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 Placeholder for missing information: Explore algorithms like K-Means (grouping data points based on similarity), Hierarchical Clustering (building a hierarchy of clusters), DBSCAN (identifying clusters of varying densities), Gaussian Mixture Models (representing data as a mixture of probability distributions), and Agglomerative Clustering (a bottom-up approach to forming clusters). 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Clustering empowers tasks like customer segmentation, image segmentation, anomaly detection, and social network analysis. ------------------------------------------------------------ Good luck with your AI research!
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Head of Cards Risk Data Lab | Global Cards | Santander HQ | Santander Young Leader 2023 | Santander Women Network | Mentor | Alumni UEvora
Straight and simple : how and where to use ML Algorithms
PhD candidate specializing in AI research | Academic Reviewer | Academic Writer | Machine Learning | Deep Learning | NLP | LLMs | RAG | Computer Vision | Medical Data Analysis
𝐓𝐨𝐩 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐚𝐧𝐝 𝐓𝐡𝐞𝐢𝐫 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Machine Learning (ML) is revolutionizing various industries, and understanding its core concepts is crucial. Here, I explore ML algorithms and their applications for three major tasks: Classification, Prediction, and Clustering, as follows: 1. 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 - Here, we assess how well an AI model categorizes data. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 • Decision Trees: Rule-based models for making classifications (e.g., spam or not spam). • Random Forest: Combines multiple decision trees for improved accuracy. • Support Vector Machines (SVM): Efficiently classifies data into categories. • K-Nearest Neighbors (KNN): Classifies data based on the majority vote of its neighbors. • Naive Bayes: Classifies data based on probabilities of features being present in a class. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 From fraud detection and medical diagnosis to image recognition and recommendation systems, classification powers diverse tasks. ------------------------------------------------------------ 2. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 - This focuses on how well an AI model forecasts future values. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 • Linear Regression: Models linear relationships between variables for continuous prediction (e.g., future sales based on historical trends). • Logistic Regression: Predicts the probability of an event occurring (e.g., customer churn). • Gradient Boosting Machines (GBM): Powerful ensemble models for complex predictions. • Neural Networks: Inspired by the human brain, they learn complex patterns from data for prediction. • ARIMA (AutoRegressive Integrated Moving Average): Models time series data for forecasting. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Prediction impacts numerous areas, including sales forecasting, stock market analysis, and medical prognosis. ------------------------------------------------------------ 3. 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 - is the process of grouping similar data points together in order to discover inherent patterns or structures within a dataset. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 Placeholder for missing information: Explore algorithms like K-Means (grouping data points based on similarity), Hierarchical Clustering (building a hierarchy of clusters), DBSCAN (identifying clusters of varying densities), Gaussian Mixture Models (representing data as a mixture of probability distributions), and Agglomerative Clustering (a bottom-up approach to forming clusters). 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Clustering empowers tasks like customer segmentation, image segmentation, anomaly detection, and social network analysis. ------------------------------------------------------------ Good luck with your AI research! #phd #phdlife #phdthesis #research #researchpaper #ai #machinelearning #deeplearning #academicwriting #ai #machinelearning #deeplearning #genai #ann #nlp #DataScience #artificialintelligence #python #pythonprogramming
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Data Scientist | Seeking to Drive Impact in New Role | Python | Machine Learning | Deep Learning | Computer Vision
🤔 𝐍𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐯𝐬. 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 𝐖𝐡𝐢𝐜𝐡 𝐒𝐡𝐨𝐮𝐥𝐝 𝐘𝐨𝐮 𝐂𝐡𝐨𝐨𝐬𝐞 𝐁𝐞𝐟𝐨𝐫𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐘𝐨𝐮𝐫 𝐌𝐋 𝐌𝐨𝐝𝐞𝐥? When prepping your data for machine learning, one key decision is whether to use 𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 or 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧. Both are essential techniques for scaling, but they shine in different scenarios. Let’s break it down: 🔄 𝐍𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (𝐌𝐢𝐧-𝐌𝐚𝐱 𝐒𝐜𝐚𝐥𝐢𝐧𝐠): This technique scales your data into a fixed range, typically [0, 1]. It’s useful when your features have different ranges, and you need to bring them to a consistent scale, especially for algorithms that rely on distance metrics. 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐔𝐬𝐞: - Working with algorithms like 𝐤-𝐍𝐍, 𝐧𝐞𝐮𝐫𝐚𝐥 𝐧𝐞𝐭𝐰𝐨𝐫𝐤𝐬, or 𝐥𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐫𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧, where small differences in scale can influence distance-based calculations. - Your data doesn't follow a 𝐧𝐨𝐫𝐦𝐚𝐥 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧. - You know your data has predefined bounds, and scaling them consistently improves performance. 💡 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: If you have a feature that ranges from 50 to 1000 and another from 1 to 20, normalization will adjust these features to a comparable scale, ensuring no single feature dominates. ⚖️ 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (𝐙-𝐬𝐜𝐨𝐫𝐞 𝐒𝐜𝐚𝐥𝐢𝐧𝐠): This method centers your data around a mean of 0 and a standard deviation of 1. It’s ideal for algorithms that assume your data is normally distributed or rely on the spread of the data. 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐔𝐬𝐞: - Algorithms like 𝐒𝐕𝐌, 𝐏𝐂𝐀, and 𝐥𝐢𝐧𝐞𝐚𝐫/𝐥𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐫𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 tend to perform better when data is standardized. - Your data follows a 𝐧𝐨𝐫𝐦𝐚𝐥 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧, or you want to remove the influence of outliers on model training. - When dealing with features that vary widely in units or magnitude, and you need them on the same scale without specific bounds. 💡 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: If you have a dataset of heights in centimeters and incomes in thousands of dollars, standardization helps bring them onto the same level without limiting their range. 🧠 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: - Use 𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 when you need to scale data to a range and care about relative distances (e.g., k-NN, neural nets). - Opt for 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧 when your data is normally distributed or algorithms assume Gaussian-like features (e.g., SVM, PCA). Choosing the right scaling method is not just a technical detail—it’s the first step toward a high-performing model! 💪 #datascience #normalization #standardization #machinelearning #featureengineering #ai #mltips #datascaling
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Not every classification problem needs be solved with a Neural Network. SVM is one of my favorite alternatives which is nicely summarized in this article. #svm #machinelearning #datascience #ai #ml #artificialintelligence #algorithms #computerscience https://lnkd.in/gMJnZeCJ
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Learn AI Together | Sharing My Journey in AI, Data Science, and Core Machine Learning | MLOps Practitioner
𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗠𝗟 𝗦𝗸𝗶𝗹𝗹𝘀: Essential Hyperparameter Insights! Pro Tip: Hyperparameters can significantly impact your model’s performance. Experiment with different combinations to find what works best for your data. 1️⃣ Linear & Logistic Regression: * L1/L2 Penalty: Regularization techniques to prevent overfitting. * Solver: Optimization algorithm for finding model coefficients. * Class Weight (Logistic): Adjusts weights inversely proportional to class frequencies. 2️⃣ Naive Bayes: * Alpha: Smoothing parameter to handle zero frequencies. * Fit Prior: Option to learn class priors or not. 3️⃣ Decision Trees & Random Forests: * Criterion: Metric to measure the quality of splits (e.g., Gini, Entropy). * Max Depth: Maximum depth of the tree to control overfitting. * Min Sample Split: Minimum number of samples required to split a node. 4️⃣ Gradient Boosted Trees: * Learning Rate: Shrinks contribution of each tree to prevent overfitting. * N Estimators: Number of boosting stages to perform. 5️⃣ Principal Component Analysis: * N Component: Number of principal components to keep. * SVD Solver: Algorithm for computing the singular value decomposition. 6️⃣ K-Nearest Neighbor: * N Neighbors: Number of neighbors to use for k-nearest neighbors. * Weights: Function to weight points (e.g., uniform, distance). 7️⃣ K-Means: * N Clusters: Number of clusters to form. * Init: Method for initialization (e.g., k-means++, random). 8️⃣ Dense Neural Networks: * Hidden Layer Sizes: Number of neurons in each hidden layer. * Activation: Activation function for the hidden layers. * Dropout: Fraction of input units to drop to prevent overfitting. 💡 Why It Matters: * Fine-tuning hyperparameters can lead to more accurate models. * It helps in avoiding overfitting and underfitting. * Essential for optimizing model performance and achieving better results --- Tagging some creators I follow, who post amazing content - - Kirk Borne, Ph.D. - Pratham Kohli - POOJA JAIN - Brij kishore Pandey - Gregor Ojstersek - Isha Rani - Danny Ma - Hina Arora - Alex Wang - John Crickett - Gina Acosta Gutiérrez - Dawn Choo - Omar Halabieh For more on AI/ML/Data Science/Data Analyst and learning materials, please check my previous posts. Click "Follow" and let's grow together! Neeraj .. #MachineLearning #DataScience #AI #BigData #DeepLearning #ArtificialIntelligence #Analytics #DataAnalytics #DataMining #DataVisualization #DataEngineer #Tech #Python #DataScienceCommunity #ML
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12 Machine Learning Algorithms Explained: From Basics to Brainpower 𝟭. 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Picture drawing a line through data points. This classic algorithm predicts future values, making it a foundational tool. 𝟮. 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Think sorting apples and oranges. This algorithm categorizes data, helping you understand "yes" or "no" situations. 𝟯. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲: Imagine playing "20 Questions" with your data. This algorithm asks questions to make decisions and predictions, branching out like a tree. 𝟰. 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁: Combine multiple decision trees, and you get a "forest" of predictions! This robust method often outperforms individual trees. 𝟱. 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗩𝗲𝗰𝘁𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 (𝗦𝗩𝗠𝘀): Picture strategists finding boundaries. SVMs excel at drawing clear lines between different groups of data points. 𝟲. 𝗞-𝗡𝗲𝗮𝗿𝗲𝘀𝘁 𝗡𝗲𝗶𝗴𝗵𝗯𝗼𝗿𝘀: Like finding your closest friends, this algorithm predicts your group based on the "nearest neighbors" in your data. 𝟳. 𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀: Imagine continuously getting smarter. This algorithm learns step-by-step, minimizing mistakes to make better predictions over time. 𝟴. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Delve into the brain-like power of neural networks. This complex method excels at recognizing patterns and insights in data like images and sounds. 𝟵. 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗣𝗖𝗔): Feeling overwhelmed by complex data? PCA simplifies it by focusing on the most important pieces, making it easier to analyze and visualize. 𝟭𝟬. 𝗡𝗮𝗶𝘃𝗲 𝗕𝗮𝘆𝗲𝘀: Need a quick prediction? This algorithm uses probability and assumptions of independence to make speedy estimates. 𝟭𝟭. 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺: Ever sorted things instinctively into groups? That's clustering! This algorithm finds hidden patterns in data, grouping similar things together automatically. 𝟭𝟮. 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸: The core of deep learning, neural networks are inspired by the brain's interconnectedness. They power complex problem-solving, mimicking the way our brains learn. 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: Choosing the right algorithm depends on your specific data and goals. This is just a starting point to explore the exciting world of machine learning! #mlops #ml #mlalgorithms #mlengineer #machinelearningengineer #machinelearningalgorithms #machinelearningmodels #machinelearning #mlmodels
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7.5M Reach Finance Consultant | Business Operations Specialist | AI Savvy Digital Marketer
12 Machine Learning Algorithms Explained: From Basics to Brainpower 𝟭. 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Picture drawing a line through data points. This classic algorithm predicts future values, making it a foundational tool. 𝟮. 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Think sorting apples and oranges. This algorithm categorizes data, helping you understand "yes" or "no" situations. 𝟯. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲: Imagine playing "20 Questions" with your data. This algorithm asks questions to make decisions and predictions, branching out like a tree. 𝟰. 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁: Combine multiple decision trees, and you get a "forest" of predictions! This robust method often outperforms individual trees. 𝟱. 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗩𝗲𝗰𝘁𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 (𝗦𝗩𝗠𝘀): Picture strategists finding boundaries. SVMs excel at drawing clear lines between different groups of data points. 𝟲. 𝗞-𝗡𝗲𝗮𝗿𝗲𝘀𝘁 𝗡𝗲𝗶𝗴𝗵𝗯𝗼𝗿𝘀: Like finding your closest friends, this algorithm predicts your group based on the "nearest neighbors" in your data. 𝟳. 𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀: Imagine continuously getting smarter. This algorithm learns step-by-step, minimizing mistakes to make better predictions over time. 𝟴. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Delve into the brain-like power of neural networks. This complex method excels at recognizing patterns and insights in data like images and sounds. 𝟵. 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗣𝗖𝗔): Feeling overwhelmed by complex data? PCA simplifies it by focusing on the most important pieces, making it easier to analyze and visualize. 𝟭𝟬. 𝗡𝗮𝗶𝘃𝗲 𝗕𝗮𝘆𝗲𝘀: Need a quick prediction? This algorithm uses probability and assumptions of independence to make speedy estimates. 𝟭𝟭. 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺: Ever sorted things instinctively into groups? That's clustering! This algorithm finds hidden patterns in data, grouping similar things together automatically. 𝟭𝟮. 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸: The core of deep learning, neural networks are inspired by the brain's interconnectedness. They power complex problem-solving, mimicking the way our brains learn. 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: Choosing the right algorithm depends on your specific data and goals. This is just a starting point to explore the exciting world of machine learning! #mlops #ml #mlalgorithms #mlengineer #machinelearningengineer #machinelearningalgorithms #machinelearningmodels #machinelearning #mlmodels
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Attended GLS University
6moHello,I am Jayita Das from Konzept Solutions. I trust this message finds you well. I am reaching out to inform you about our professional website and mobile development services. If you are interested in any of our services please let me know . I would be more than happy to discuss further details with you.Thank you.